AITProject

gun_violence_df <- read_csv("gun-violence-data_01-2013_03-2018.csv")

## Rows: 239677 Columns: 29
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (18): state, city_or_county, address, incident_url, source_url, gun_sto...
## dbl   (9): incident_id, n_killed, n_injured, congressional_district, latitud...
## lgl   (1): incident_url_fields_missing
## date  (1): date
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

head(gun_violence_df)

## # A tibble: 6 × 29
##   incident_id date       state         city_or_county address n_killed n_injured
##         <dbl> <date>     <chr>         <chr>          <chr>      <dbl>     <dbl>
## 1      461105 2013-01-01 Pennsylvania  Mckeesport     1506 V…        0         4
## 2      460726 2013-01-01 California    Hawthorne      13500 …        1         3
## 3      478855 2013-01-01 Ohio          Lorain         1776 E…        1         3
## 4      478925 2013-01-05 Colorado      Aurora         16000 …        4         0
## 5      478959 2013-01-07 North Caroli… Greensboro     307 Mo…        2         2
## 6      478948 2013-01-07 Oklahoma      Tulsa          6000 b…        4         0
## # ℹ 22 more variables: incident_url <chr>, source_url <chr>,
## #   incident_url_fields_missing <lgl>, congressional_district <dbl>,
## #   gun_stolen <chr>, gun_type <chr>, incident_characteristics <chr>,
## #   latitude <dbl>, location_description <chr>, longitude <dbl>,
## #   n_guns_involved <dbl>, notes <chr>, participant_age <chr>,
## #   participant_age_group <chr>, participant_gender <chr>,
## #   participant_name <chr>, participant_relationship <chr>, …

gun_violence_df <- subset(gun_violence_df, select = -c(address,incident_url, source_url,incident_url_fields_missing,congressional_district, location_description, notes, n_guns_involved,participant_name,sources, state_house_district, state_senate_district,participant_relationship))

Q. What are the geographic patterns of gun violence incidents in the United States? Are certain states, cities, or neighborhoods more likely to experience incidents?

unique_values <- table(gun_violence_df$state)

death_injured_count <- data.frame(unique_values)

total_killed <- aggregate(cbind(gun_violence_df$n_killed, gun_violence_df$n_injured) ~ gun_violence_df$state, data = gun_violence_df, FUN = sum)

total_killed <- total_killed %>% 
        rename("states" = "gun_violence_df$state",
               "total_killed" = "V1",
               "total_injured" = "V2")

total_killed$total_cases <- total_killed$total_killed + total_killed$total_injured

my_sf_layer_wgs84 <- inner_join(my_sf_layer_wgs84,total_killed, by = c("NAME" = "states"))

pal2 <- colorNumeric("plasma", domain = my_sf_layer_wgs84$total_cases, reverse = TRUE)
labels <- sprintf(
  "<strong>State: %s</strong><br/><strong>Total Injured: </strong>%s<br/><strong>Total Killed: </strong>%s</sup>",
  my_sf_layer_wgs84$NAME, my_sf_layer_wgs84$total_injured, my_sf_layer_wgs84$total_killed) %>% lapply(htmltools::HTML)

leaflet(my_sf_layer_wgs84) %>%
  addTiles() %>% 
  addPolygons(fillColor = ~pal2(my_sf_layer_wgs84$total_cases),
              fillOpacity = .5,
              color = "black",
              weight = 0.4,
              label = labels,
              dashArray = "5",
              highlightOptions = highlightOptions(
                weight = 8,
                color = "skyblue",
                dashArray = "",
                fillOpacity = 0.7,
                bringToFront = TRUE),
              labelOptions = labelOptions(
                style = list("font-weight" = "normal", padding = "3px 8px"),
                textsize = "10px",
                direction = "auto")) %>% 
  addLegend('topright',
            pal = pal2,
            values =~my_sf_layer_wgs84$total_cases,
            title = 'Gun Violence in US(2013-18)')%>%
  addMiniMap(
    tiles = providers$Esri.WorldStreetMap,
    toggleDisplay = TRUE
  )%>%
  addEasyButton(easyButton(
    icon="fa-globe", title="Zoom to Level 1",
    onClick=JS("function(btn, map){ map.setZoom(2); }")))

From the above choropleth map, it can be seen that there are high chances of crime happening in states such as Illinois which has a highest record of Incidents followed by California, Texas and Florida. This information can be useful in inspecting gun control policies and gun licence distribution in these states to lower the crime rate and taking measures for public safety.

total_fatalities_county <- aggregate(cbind(gun_violence_df$n_killed, gun_violence_df$n_injured) ~ gun_violence_df$city_or_county, data = gun_violence_df, FUN = sum)

total_fatalities_county <- total_fatalities_county %>% 
        rename("county" = "gun_violence_df$city_or_county",
               "total_killed" = "V1",
               "total_injured" = "V2")

total_fatalities_county$total_count <- total_fatalities_county$total_killed + total_fatalities_county$total_injured

county_df <- map_data("county") #county data

county_df <- subset(county_df, select = -c(group,order))

total_fatalities_county$county <- tolower(total_fatalities_county$county)

total_fatalities_county <- inner_join(
  total_fatalities_county,
  county_df,
  by = c("county" = "subregion"),
  copy = FALSE,
  na_matches = c("na", "never"),
  multiple = "first",
  unmatched = "drop",
  relationship = NULL
)

label2 <- sprintf(
  "<strong>County: %s</strong><br/><strong>Total Fatalities: </strong>%s<br/><strong>Total Killed: </strong>%s<br/><strong>Total Injured: </strong>%s</sup>",
  total_fatalities_county$county, total_fatalities_county$total_count, total_fatalities_county$total_killed, total_fatalities_county$total_injured) %>% lapply(htmltools::HTML)

my_palette <- colorNumeric(c("orange", "red3", "maroon4"), domain=range(total_fatalities_county$total_count))

leaflet(total_fatalities_county) %>%
  addProviderTiles(providers$Stamen.TonerLite) %>% 
  addCircles(lng = ~long,
             lat = ~lat,
             popup = label2,
             label = ~county,
             opacity = 0.4, 
             radius = total_fatalities_county$total_count*50,
             color = ~my_palette(total_fatalities_county$total_count),
             fillColor = ~my_palette(total_fatalities_county$total_count),
             fillOpacity = 0.25
  )%>% 
  addLegend('bottomleft',
            pal = my_palette,
            values = range(total_fatalities_county$total_count),
            title = 'County Fatalities Across US(2013-18)')

From the above leaflet, we can see a different scenario where even though states such as Illinois and California had the most incidents across the state, when we compare across counties, the highest count of incidents are occured in Baltimore followed by Philadelphia and Houston. With the help of both these graphs we can see the various aspects of Geographical patterns of Gun Violence Incidents across US.

Q. What are the temporal patterns of gun violence incidents in the United States? Are there certain months, days of the week when incidents are more likely to occur?

gun_violence_df$date <- as.Date(gun_violence_df$date)

gun_violence_df$year <- format(gun_violence_df$date, "%Y")
gun_violence_df$month <- format(gun_violence_df$date, "%m")
gun_violence_df$day <- format(gun_violence_df$date, "%A")

y_yrs <- tapply(gun_violence_df$incident_id, gun_violence_df$year, FUN = length)
x_yrs <- unique(gun_violence_df$year)

y_months <- aggregate(gun_violence_df$incident_id, by = list(gun_violence_df$year, gun_violence_df$month), FUN = length)
y_months <- tapply(y_months$x, y_months$Group.2, mean)
x_months <- c('Jan', 'Feb', 'Mar', 'Apr', 'May', 'June', 'July', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec')

y_days <- aggregate(gun_violence_df$incident_id, by = list(gun_violence_df$year, gun_violence_df$day), FUN = length)
y_days <- tapply(y_days$x, y_days$Group.2, mean)
x_days <- c('Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday')


plot1 <- ggplot(data = gun_violence_df, aes(x = year)) +
  geom_bar(stat = "count", fill="#06C4BE") +
  geom_text(stat='count',aes(label=after_stat(count), vjust=1.5, hjust=0.5), size= 2.5, colour="white")+
  theme_minimal()+
  labs(title = "Number of Incidents per Year", x = "YEAR", y = "COUNT")

plot2 <- ggplot(data = gun_violence_df, aes(x = month)) +
  geom_bar(stat = "count", fill ="maroon4") +
  geom_text(stat='count',aes(label=after_stat(count), vjust=1.5, hjust=0.5), size= 2.5, colour="white")+
  theme_minimal()+
  labs(title = "Number of Incidents per Month", x = "MONTH", y = "COUNT")+
  scale_fill_viridis_c(option = "magma")

plot3 <- ggplot(data = gun_violence_df, aes(x = day)) +
  geom_bar(stat = "count", fill="orange") +
  geom_text(stat='count',aes(label=after_stat(count), vjust=1.5, hjust=0.5), size= 2.5, colour="white")+
  theme_minimal()+
  labs(title = "Number of Incidents per Day", x = "DAY", y = "COUNT")

grid.arrange(plot1, plot2, plot3, nrow = 3)

Incidents per Year: 1. The gun violence incidents per year show an upward trend from 51000 in 2014 to 61000 in 2017. 2. Although the data for 2018 is insufficient(only for first 3 months of 2018), there are already 13000 incidents have taken place.

Average Number of Incidents per Month Over the Years: 1. From the graph, it can be seen that the count of incidents is higher around January and March further followed by July and August months.

Average Number of Incidents per Day over Years: 1. From observation, we can see that most of the incidents have occured during the weekend making Saturday and Sunday most susceptible to happening of a crime.